With the fast growth of video-capturing devices, dramatic increase of network bandwidth and various forms of video-sharing social network, the number of available video content online and offline rapidly grows in recent years. Following this trend, the need for annotation of this large number of videos becomes high demanding for widely range of applications. Manual annotation for video is very time consuming with high cost of effort. Hence, automatic annotation in video has attracted a lot of attention in recent years. Human face is one of the most important and frequent object in videos. Therefore, automatic annotation on human has potentially large number of applications such as security, rich content generation on movies, medical analysis, video documentation and archiving.
However, face annotation in video often is a problem in computer vision that aims at locating and identifying human faces (i.e., giving the identity) in a video sequence using certain knowledge set with known identities (e.g., labeled images as training data). Face Recognition (FR) is an important component of face annotation. In general, there are two types of face recognition tasks. One type is FR in constrained environment which refers to the faces to be recognized existing in relatively stable and static background, and the other type is FR in unconstrained environment which refers to the faces to be recognized existing in the background which is non-stable with dynamic changes. The FR in unconstrained environment is much more challenging due to the large variation in terms of orientation, luminance, and expression, etc. The Face annotation in video belongs to the unconstrained category due to the nature of the various forms of videos. In order to improve the annotation accuracy, existing methods with various frameworks, features and classifiers typically involve some manual work to produce training data (i.e., labeled face images).
The disclosed methods and systems are directed to solve one or more problems set forth above and other problems.